Selective-plane-activation structured illumination microscopy.

The background light from out-of-focus planes hinders resolution enhancement in structured illumination microscopy when observing volumetric samples. Here we used selective plane illumination and reversibly photoswitchable fluorescent proteins to realize structured illumination within the focal plane and eliminate the out-of-focus background. Theoretical investigation of the imaging properties and experimental demonstrations show that selective plane activation is beneficial for imaging dense microstructures in cells and cell spheroids.

Open-3DSIM: an open-source three-dimensional structured illumination microscopy reconstruction platform.

Open-3DSIM is an open-source reconstruction platform for three-dimensional structured illumination microscopy. We demonstrate its superior performance for artifact suppression and high-fidelity reconstruction relative to other algorithms on various specimens and over a range of signal-to-noise levels. Open-3DSIM also offers the capacity to extract dipole orientation, paving a new avenue for interpreting subcellular structures in six dimensions (xyzθλt). The platform is available as MATLAB code, a Fiji plugin and an Exe application to maximize user-friendliness.

Illuminating cell signalling with optogenetic tools.

The light-based control of ion channels has been transformative for the neurosciences, but the optogenetic toolkit does not stop there. An expanding number of proteins and cellular functions have been shown to be controlled by light, and the practical considerations in deciding between reversible optogenetic systems (such as systems that use light-oxygen-voltage domains, phytochrome proteins, cryptochrome proteins and the fluorescent protein Dronpa) are well defined. The field is moving beyond proof of concept to answering real biological questions, such as how cell signalling is regulated in space and time, that were difficult or impossible to address with previous tools.

High correlated color temperature artificial lighting impairs retinal pigment epithelium integrity and chloride ion transport: A potential mechanism for choroidal thinning.

Among the environmental factors contributing to myopia, the role of correlated color temperature (CCT) of ambient light emerges as a key element warranting in-depth investigation. The choroid, a highly vascularized and dynamic structure, often undergoes thinning during the progression of myopia, though the precise mechanism remains elusive. The retinal pigment epithelium (RPE), the outermost layer of the retina, plays a pivotal role in regulating the transport of ion and fluid between the subretinal space and the choroid. A hypothesis suggests that variations in choroidal thickness (ChT) may be modulated by transepithelial fluid movement across the RPE. Our experimental results demonstrate that high CCT illumination significantly compromised the integrity of tight junctions in the RPE and disrupted chloride ion transport. This functional impairment of the RPE may lead to a reduction in fluid transfer across the RPE, consequently resulting in choroidal thinning and potentially accelerating axial elongation. Our findings provide support for the crucial role of the RPE in regulating ChT. Furthermore, we emphasize the potential hazards posed by high CCT artificial illumination on the RPE, the choroid, and refractive development, underscoring the importance of developing eye-friendly artificial light sources to aid in the prevention and control of myopia.

Improving strawberry plant resilience to salinity and alkalinity through the use of diverse spectra of supplemental lighting.

BACKGROUND: This study explores the impact of various light spectra on the photosynthetic performance of strawberry plants subjected to salinity, alkalinity, and combined salinity/alkalinity stress. We employed supplemental lighting through Light-emitting Diodes (LEDs) with specific wavelengths: monochromatic blue (460 nm), monochromatic red (660 nm), dichromatic blue/red (1:3 ratio), and white/yellow (400-700 nm), all at an intensity of 200 µmol m-2 S-1. Additionally, a control group (ambient light) without LED treatment was included in the study. The tested experimental variants were: optimal growth conditions (control), alkalinity (40 mM NaHCO3), salinity (80 mM NaCl), and a combination of salinity/alkalinity. RESULTS: The results revealed a notable decrease in photosynthetic efficiency under both salinity and alkalinity stresses, especially when these stresses were combined, in comparison to the no-stress condition. However, the application of supplemental lighting, particularly with the red and blue/red spectra, mitigated the adverse effects of stress. The imposed stress conditions had a detrimental impact on both gas exchange parameters and photosynthetic efficiency of the plants. In contrast, treatments involving blue, red, and blue/red light exhibited a beneficial effect on photosynthetic efficiency compared to other lighting conditions. Further analysis of JIP-test parameters confirmed that these specific light treatments significantly ameliorated the stress impacts. CONCLUSIONS: In summary, the utilization of blue, red, and blue/red light spectra has the potential to enhance plant resilience in the face of salinity and alkalinity stresses. This discovery presents a promising strategy for cultivating plants in anticipation of future challenging environmental conditions.

LEDs for photons, physiology and food.

Lighting based on light-emitting diodes (LEDs) not only is more energy efficient than traditional lighting, but also enables improved performance and control. The colour, intensity and distribution of light can now be controlled with unprecedented precision, enabling light to be used both as a signal for specific physiological responses in humans and plants, and as an efficient fuel for fresh food production. Here we show how a broad and improved understanding of the physiological responses to light will facilitate greater energy savings and provide health and productivity benefits that have not previously been associated with lighting.

Skin Tone Analysis Through Skin Tone Map Generation With Optical Approach and Deep Learning.

BACKGROUND: Skin tone assessment is critical in both cosmetic and medical fields, yet traditional methods like the individual typology angle (ITA) have limitations, such as sensitivity to illuminants and insensitivity to skin redness. METHODS: This study introduces an automated image-based method for skin tone mapping by applying optical approaches and deep learning. The method generates skin tone maps by leveraging the illuminant spectrum, segments the skin region from face images, and identifies the corresponding skin tone on the map. The method was evaluated by generating skin tone maps under three standard illuminants (D45, D65, and D85) and comparing the results with those obtained using ITA on skin tone simulation images. RESULTS: The results showed that skin tone maps generated under the same lighting conditions as the image acquisition (D65) provided the highest accuracy, with a color difference of around 6, which is more than twice as small as those observed under other illuminants. The mapping positions also demonstrated a clear correlation with pigment levels. Compared to ITA, the proposed approach was particularly effective in distinguishing skin tones related to redness. CONCLUSION: Despite the need to measure the illuminant spectrum and for further physiological validation, the proposed approach shows potential for enhancing skin tone assessment. Its ability to mitigate the effects of illuminants and distinguish between the two dominant pigments offers promising applications in both cosmetic and medical diagnostics.

Investigating the effects of indoor lighting on measures of brain health in older adults: protocol for a cross-over randomized controlled trial.

BACKGROUND: The worldwide number of adults aged 60 years and older is expected to double from 1 billion in 2019 to 2.1 billion by 2050. As the population lives longer, the rising incidence of chronic diseases, cognitive disorders, and behavioral health issues threaten older adults' health span. Exercising, getting sufficient sleep, and staying mentally and socially active can improve quality of life, increase independence, and potentially lower the risk for Alzheimer's disease or other dementias. Nonpharmacological approaches might help promote such behaviors. Indoor lighting may impact sleep quality, physical activity, and cognitive function. Dynamically changing indoor lighting brightness and color throughout the day has positive effects on sleep, cognitive function, and physical activity of its occupants. The aim of this study is to investigate how different indoor lighting conditions affect such health measures to promote healthier aging. METHODS: This protocol is a randomized, cross-over, single-site trial followed by an exploratory third intervention. Up to 70 older adults in independent living residences at a senior living facility will be recruited. During this 16-week study, participants will experience three lighting conditions. Two cohorts will first experience a static and a dynamic lighting condition in a cluster-randomized cross-over design. The static condition lighting will have fixed brightness and color to match lighting typically provided in the facility. For the dynamic condition, brightness and color will change throughout the day with increased brightness in the morning. After the cross-over, both cohorts will experience another dynamic lighting condition with increased morning brightness to determine if there is a saturation effect between light exposure and health-related measures. Light intake, sleep quality, and physical activity will be measured using wearable devices. Sleep, cognitive function, mood, and social engagement will be assessed using surveys and cognitive assessments. DISCUSSION: We hypothesize participants will have better sleep quality and greater physical activity during the dynamic lighting compared to the static lighting condition. Additionally, we hypothesize there is a maximal threshold at which health-outcomes improve based on light exposure. Study findings may identify optimal indoor lighting solutions to promote healthy aging for older adults. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05978934.

Color constancy mechanisms in virtual reality environments.

Prior research has demonstrated high levels of color constancy in real-world scenarios featuring single light sources, extensive fields of view, and prolonged adaptation periods. However, exploring the specific cues humans rely on becomes challenging, if not unfeasible, with actual objects and lighting conditions. To circumvent these obstacles, we employed virtual reality technology to craft immersive, realistic settings that can be manipulated in real time. We designed forest and office scenes illuminated by five colors. Participants selected a test object most resembling a previously shown achromatic reference. To study color constancy mechanisms, we modified scenes to neutralize three contributors: local surround (placing a uniform-colored leaf under test objects), maximum flux (keeping the brightest object constant), and spatial mean (maintaining a neutral average light reflectance), employing two methods for the latter: changing object reflectances or introducing new elements. We found that color constancy was high in conditions with all cues present, aligning with past research. However, removing individual cues led to varied impacts on constancy. Local surrounds significantly reduced performance, especially under green illumination, showing strong interaction between greenish light and rose-colored contexts. In contrast, the maximum flux mechanism barely affected performance, challenging assumptions used in white balancing algorithms. The spatial mean experiment showed disparate effects: Adding objects slightly impacted performance, while changing reflectances nearly eliminated constancy, suggesting human color constancy relies more on scene interpretation than pixel-based calculations.

Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion.

The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human-computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human-computer interaction, sports rehabilitation, and virtual reality.

Localization microscopy at doubled precision with patterned illumination.

MINFLUX offers a breakthrough in single molecule localization precision, but is limited in field of view. Here we combine centroid estimation and illumination pattern induced photon count variations in a conventional widefield imaging setup to extract position information over a typical micrometer-sized field of view. We show a near two-fold improvement in precision over standard localization with the same photon count on DNA-origami nanostructures and tubulin in cells, using DNA-PAINT and STORM imaging.

Scanless two-photon excitation with temporal focusing.

Temporal focusing, with its ability to focus light in time, enables scanless illumination of large surface areas at the sample with micrometer axial confinement and robust propagation through scattering tissue. In conventional two-photon microscopy, widely used for the investigation of intact tissue in live animals, images are formed by point scanning of a spatially focused pulsed laser beam, resulting in limited temporal resolution of the excitation. Replacing point scanning with temporally focused widefield illumination removes this limitation and represents an important milestone in two-photon microscopy. Temporal focusing uses a diffusive or dispersive optical element placed in a plane conjugate to the objective focal plane to generate position-dependent temporal pulse broadening that enables axially confined multiphoton absorption, without the need for tight spatial focusing. Many techniques have benefitted from temporal focusing, including scanless imaging, super-resolution imaging, photolithography, uncaging of caged neurotransmitters and control of neuronal activity via optogenetics.

Differential synthetic illumination based on multi-line detection for resolution and contrast enhancement of line confocal microscopy.

Line confocal (LC) microscopy is a fast 3D imaging technique, but its asymmetric detection slit limits resolution and optical sectioning. To address this, we propose the differential synthetic illumination (DSI) method based on multi-line detection to enhance the spatial resolution and optical sectioning capability of the LC system. The DSI method allows the imaging process to simultaneously accomplish on a single camera, which ensures the rapidity and stability of the imaging process. DSI-LC improves X- and Z-axis resolution by 1.28 and 1.26 times, respectively, and optical sectioning by 2.6 times compared to LC. Furthermore, the spatially resolved power and contrast are also demonstrated by imaging pollen, microtubule, and the fiber of the GFP fluorescence-labeled mouse brain. Finally, Video-rate imaging of zebrafish larval heart beating in a 665.6 × 332.8 µm2 field-of-view is achieved. DSI-LC provides a promising approach for 3D large-scale and functional imaging in vivo with improved resolution, contrast, and robustness.

Motion-resistant structured illumination microscopy based on principal component analysis.

Structured illumination microscopy (SIM) has become one of the most significant super-resolution techniques in bioscience for observing live-cell dynamics, thanks to fast full-field imaging and low photodamage. However, artifact-free SIM super-resolution reconstruction requires precise knowledge about variable environment-sensitive illumination parameters. Conventional algorithms typically, under the premise of known and reliable constant phase shifts, compensate for residual parameters, which will be easily broken by motion factors such as environment and medium perturbations, and sample offsets. In this Letter, we propose a robust motion-resistant SIM algorithm based on principal component analysis (mrPCA-SIM), which can efficiently compensate for nonuniform pixel shifts and phase errors in each raw illumination image. Experiments demonstrate that mrPCA-SIM achieves more robust imaging quality in complex, unstable conditions compared with conventional methods, promising a more compatible and flexible imaging tool for live cells.

Detection of Defective Lettuce Seedlings Grown in an Indoor Environment under Different Lighting Conditions Using Deep Learning Algorithms.

Sorting seedlings is laborious and requires attention to identify damage. Separating healthy seedlings from damaged or defective seedlings is a critical task in indoor farming systems. However, sorting seedlings manually can be challenging and time-consuming, particularly under complex lighting conditions. Different indoor lighting conditions can affect the visual appearance of the seedlings, making it difficult for human operators to accurately identify and sort the seedlings consistently. Therefore, the objective of this study was to develop a defective-lettuce-seedling-detection system under different indoor cultivation lighting systems using deep learning algorithms to automate the seedling sorting process. The seedling images were captured under different indoor lighting conditions, including white, blue, and red. The detection approach utilized and compared several deep learning algorithms, specifically CenterNet, YOLOv5, YOLOv7, and faster R-CNN to detect defective seedlings in indoor farming environments. The results demonstrated that the mean average precision (mAP) of YOLOv7 (97.2%) was the highest and could accurately detect defective lettuce seedlings compared to CenterNet (82.8%), YOLOv5 (96.5%), and faster R-CNN (88.6%). In terms of detection under different light variables, YOLOv7 also showed the highest detection rate under white and red/blue/white lighting. Overall, the detection of defective lettuce seedlings by YOLOv7 shows great potential for introducing automated seedling-sorting systems and classification under actual indoor farming conditions. Defective-seedling-detection can improve the efficiency of seedling-management operations in indoor farming.

Large-field lattice structured illumination microscopy.

In this paper, we present large-field, five-step lattice structured illumination microscopy (Lattice SIM). This method utilizes a 2D grating for lattice projection and a spatial light modulator (SLM) for phase shifting. Five phase-shifted intensity images are recorded to reconstruct a super-resolution image, enhancing the imaging speed and reducing the photo-bleaching both by 17%, compared to conventional two-direction and three-shift SIM. Furthermore, lattice SIM has a three-fold spatial bandwidth product (SBP) enhancement compared to SLM/DMD-based SIM, of which the fringe number is limited by the SLM/DMD pixel number. We believe that the proposed technique will be further developed and widely applied in many fields.

Total variation and spatial iteration-based 3D structured illumination microscopy.

Three-dimensional structured illumination microscopy (3D-SIM) plays an essential role in biological volumetric imaging with the capabilities of improving lateral and axial resolution. However, the traditional linear 3D algorithm is sensitive to noise and generates artifacts, while the low temporal resolution hinders live-cell imaging. In this paper, we propose a novel 3D-SIM algorithm based on total variation (TV) and fast iterative shrinkage threshold algorithm (FISTA), termed TV-FISTA-SIM. Compared to conventional algorithms, TV-FISTA-SIM achieves higher reconstruction fidelity with the least artifacts, even when the signal-to-noise ratio (SNR) is as low as 5 dB, and a faster reconstruction rate. Through simulation, we have verified that TV-FISTA-SIM can effectively reduce the amount of required data with less deterioration. Moreover, we demonstrate TV-FISTA-SIM for high-quality multi-color 3D super-resolution imaging, which can be potentially applied to live-cell imaging applications.

Large-field structured illumination microscopy based on 2D grating and a spatial light modulator.

Structured illumination microscopy (SIM) has been widely used in biological research due to its merits of fast imaging speed, minimal invasiveness, super-resolution, and optical sectioning imaging capability. However, the conventional SIM that uses a spatial light modulator (SLM) for fringe projection often has a limited imaging field of view. Herein, we report a large-field SIM technique that combines a 2D grating for fringe pattern projection and an SLM for selecting fringe orientation and performing phase shifting digitally. The proposed SIM technique breaks the bottleneck of fringe number limited by the digital projection devices, while maintaining the advantage of high-speed (digital) phase shifting of conventional SIM. The method avoids the pixilation and dispersion effects of the SLMs. Finally, a 1.8-fold resolution enhancement in a large field of 690 × 517 µm2 under a 20×/NA0.75 objective is experimentally demonstrated. The proposed technique can be widely applied to biology, chemistry, and industry.

NIR-II multifocal structured illumination microscopy.

Optical microscopy has been widely used as a versatile tool in biological research. However, its penetration depth and spatial resolution are desperately limited by light scattering during deep propagation in turbid medium. Here, we implement near-infrared second window (1000-1700 nm) multifocal structured illumination microscopy (NIR-II MSIM) capable of deep penetration, high contrast, and enhanced spatial resolution. Raster-scanning multifocal illumination patterns ensure homogeneous illumination of the sample. By integrating NIR-II photoemission into multifocal photoexcitation, NIR-II MSIM affords deep imaging with improved lateral resolution (∼1.49 µm) at a depth of 2.5 mm in an Intralipid/agar phantom and outstanding contrast. Additionally, imaging at longer wavelength in the NIR-II region shows superior performance. This NIR-II MSIM system will afford a promising platform for studying physiological phenomena in turbid specimens in the future.